Thorsten Zenz

This paper develops a domain-adversarial neural network to predict drug combination synergy in leukemia patients using bulk transcriptomics data, showing improved accuracy over existing methods.

The paper uses drug perturbations and RNA sequencing to map pathway connectivity in CLL, showing how genetic aberrations modulate drug responses and pathway interactions.

DemoTape is a computational method for demultiplexing targeted single-cell DNA sequencing data, showing improved performance over existing methods for RNA sequencing data.

The paper develops a method to infer tumor-specific molecular dependencies by integrating drug response assays with protein-drug affinity profiles, identifying known and novel dependencies in cancer types.

This study identifies subgroup-specific gene expression profiles and mixed epistasis in chronic lymphocytic leukemia using RNA-sequencing and genomic data.

MOFA, a sparse Bayesian factor model with mixed likelihoods, separates shared from modality-specific multi-omic variation, recovering IGHV, trisomy 12 and oxidative-stress axes in 200 leukaemia samples.

This study identifies MDM4 as a critical target of 1q gain in TP53wt Burkitt lymphoma, showing its role in evading cell cycle arrest and highlighting its therapeutic potential.

The paper identifies transcript isoform changes in UQCC and RPL31 associated with SF3B1 mutations in CLL and uveal melanoma, suggesting common molecular effects across cancer types.